Predicting Antimicrobial Activity of Conjugated Oligoelectrolyte Molecules via Machine Learning.

New antibiotics are needed to battle growing antibiotic resistance, but the development process from hit, to lead, and ultimately to a useful drug takes decades. Although progress in molecular property prediction using machine-learning methods has opened up new pathways for aiding the antibiotics development process, many existing solutions rely on large data sets and finding structural similarities to existing antibiotics. Challenges remain in modeling unconventional antibiotic classes that are drawing increasing research attention. In response, we developed an antimicrobial activity prediction model for conjugated oligoelectrolyte molecules, a new class of antibiotics that lacks extensive prior structure-activity relationship studies. Our approach enables us to predict the minimum inhibitory concentration for E. coli K12, with 21 molecular descriptors selected by recursive elimination from a set of 5305 descriptors. This predictive model achieves an R2 of 0.65 with no prior knowledge of the underlying mechanism. We find the molecular representation optimum for the domain is the key to good predictions of antimicrobial activity. In the case of conjugated oligoelectrolytes, a representation reflecting the three-dimensional shape of the molecules is most critical. Although it is demonstrated with a specific example of conjugated oligoelectrolytes, our proposed approach for creating the predictive model can be readily adapted to other novel antibiotic candidate domains.

Stability follows efficiency based on the analysis of a large perovskite solar cells ageing dataset.

While perovskite solar cells have reached competitive efficiency values during the last decade, stability issues remain a critical challenge to be addressed for pushing this technology towards commercialisation. In this study, we analyse a large homogeneous dataset of Maximum Power Point Tracking (MPPT) operational ageing data that we collected with a custom-built High-throughput Ageing System in the past 3 years. In total, 2,245 MPPT ageing curves are analysed which were obtained under controlled conditions (continuous illumination, controlled temperature and atmosphere) from devices comprising various lead-halide perovskite absorbers, charge selective layers, contact layers, and architectures. In a high-level statistical analysis, we find a correlation between the maximum reached power conversion efficiency (PCE) and the relative PCE loss observed after 150-hours of ageing, with more efficient cells statistically also showing higher stability. Additionally, using the unsupervised machine learning method self-organising map, we cluster this dataset based on the degradation curve shapes. We find a correlation between the frequency of particular shapes of degradation curves and the maximum reached PCE.

Discovery of temperature-induced stability reversal in perovskites using high-throughput robotic learning.

Stability of perovskite-based photovoltaics remains a topic requiring further attention. Cation engineering influences perovskite stability, with the present-day understanding of the impact of cations based on accelerated ageing tests at higher-than-operating temperatures (e.g. 140°C). By coupling high-throughput experimentation with machine learning, we discover a weak correlation between high/low-temperature stability with a stability-reversal behavior. At high ageing temperatures, increasing organic cation (e.g. methylammonium) or decreasing inorganic cation (e.g. cesium) in multi-cation perovskites has detrimental impact on photo/thermal-stability; but below 100°C, the impact is reversed. The underlying mechanism is revealed by calculating the kinetic activation energy in perovskite decomposition. We further identify that incorporating at least 10 mol.% MA and up to 5 mol.% Cs/Rb to maximize the device stability at device-operating temperature (<100°C). We close by demonstrating the methylammonium-containing perovskite solar cells showing negligible efficiency loss compared to its initial efficiency after 1800 hours of working under illumination at 30°C.

How machine learning can help select capping layers to suppress perovskite degradation.

Environmental stability of perovskite solar cells (PSCs) has been improved by trial-and-error exploration of thin low-dimensional (LD) perovskite deposited on top of the perovskite absorber, called the capping layer. In this study, a machine-learning framework is presented to optimize this layer. We featurize 21 organic halide salts, apply them as capping layers onto methylammonium lead iodide (MAPbI3) films, age them under accelerated conditions, and determine features governing stability using supervised machine learning and Shapley values. We find that organic molecules' low number of hydrogen-bonding donors and small topological polar surface area correlate with increased MAPbI3 film stability. The top performing organic halide, phenyltriethylammonium iodide (PTEAI), successfully extends the MAPbI3 stability lifetime by 4 ± 2 times over bare MAPbI3 and 1.3 ± 0.3 times over state-of-the-art octylammonium bromide (OABr). Through characterization, we find that this capping layer stabilizes the photoactive layer by changing the surface chemistry and suppressing methylammonium loss.

Author Correction: How machine learning can help select capping layers to suppress perovskite degradation.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Enhanced visible light absorption for lead-free double perovskite Cs2AgSbBr6.

In a search for Pb-free photovoltaic materials, a double perovskite Cs2AgSbBr6 with an indirect optical bandgap of 1.64 eV has been synthesized. Single crystal X-ray diffraction determined the space group as Fm3[combining macron]m with a = 11.1583(7) Å. The black, as-synthesised compound turned brown after heat treatment at 480 K while the symmetry and crystallinity were preserved. X-ray photoelectron spectroscopy indicated the existence of Sb5+ in the black crystals, suggesting that the dark colour arises from the Sb3+-Sb5+ charge transfer. Furthermore, UV visible spectroscopy and density functional theory calculations have been applied to probe the optical properties and electronic structure.

Homogenized halides and alkali cation segregation in alloyed organic-inorganic perovskites.

The role of the alkali metal cations in halide perovskite solar cells is not well understood. Using synchrotron-based nano-x-ray fluorescence and complementary measurements, we found that the halide distribution becomes homogenized upon addition of cesium iodide, either alone or with rubidium iodide, for substoichiometric, stoichiometric, and overstoichiometric preparations, where the lead halide is varied with respect to organic halide precursors. Halide homogenization coincides with long-lived charge carrier decays, spatially homogeneous carrier dynamics (as visualized by ultrafast microscopy), and improved photovoltaic device performance. We found that rubidium and potassium phase-segregate in highly concentrated clusters. Alkali metals are beneficial at low concentrations, where they homogenize the halide distribution, but at higher concentrations, they form recombination-active second-phase clusters.